A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver

• Autorzy: Fan Liming , Wang Kunsheng , Fan Dongming

Identyfikatory

DOI

10.17531/ein.2021.1.8

• Języki publikacji: EN

Abstrakty

EN

The accurate and effective reliability prediction of light emitting diode (LED) drivers has emerged as a key issue in LED applications. However, previous studies have mainly focused on the reliability of electrolytic capacitors or other single components while ignoring circuit topology. In this study, universal generating function (UGF) and physics of failure (PoF) are integrated to predict the reliability of LED drivers. Utilizing PoF, lifetime data for each component are obtained. A system reliability model with multi-phase is established, and system reliability can be predicted using UGF. Illustrated by a two-channel LED driver, the beneficial effects of capacitors and MOSFETs for the reliability of LED drivers is verified. This study (i) provides a universal numerical approach to predict the lifetime of LED drivers considering circuit topology, (ii) enhances the modelling and reliability evaluation of circuits, and (iii) bridges the gap between component and circuit system levels.

Słowa kluczowe

EN

reliability prediction; LED driver; reliability modelling; physics of failure; universal generating function

• Wydawca: Polskie Naukowo-Techniczne Towarzystwo Eksploatacyjne
• Czasopismo: Eksploatacja i Niezawodność
• Rocznik: 2021
• Tom: Vol. 23, no. 1
• Strony: 74--83
• Opis fizyczny: Bibliogr. 41 poz., rys., tab.

Twórcy

autor

Fan Liming

• China Aerospace Academy of Systems Science and Engineering, Beijing, 100048, P. R. China

autor

Wang Kunsheng

• China Aerospace Academy of Systems Science and Engineering, Beijing, 100048, P. R. China

autor

Fan Dongming

• School of Reliability and Systems Engineering, Beihang University, Beijing, PR China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).